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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data
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| 英文论文题目: |
Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data
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| 论文题目英文: |
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| 作者: |
Zhu, Yuanheng
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| 论文出处: |
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| 刊物名称: |
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 年: |
2017
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| 卷: |
28 |
| 期: |
3 |
| 页: |
714-725 |
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| 影响因子: |
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| 摘要: |
H-infinity control is a powerful method to solve the disturbance attenuation problems that occur in some control systems. The design of such controllers relies on solving the zero-sum game (ZSG). But in practical applications, the exact dynamics is mostly unknown. Identification of dynamics also produces errors that are detrimental to the control performance. To overcome this problem, an iterative adaptive dynamic programming algorithm is proposed in this paper to solve the continuous-time, unknown nonlinear ZSG with only online data. A model-free approach to the Hamilton-Jacobi-Isaacs equation is developed based on the policy iteration method. Control and disturbance policies and value are approximated by neural networks (NNs) under the critic-actor-disturber structure. The NN weights are solved by the least-squares method. According to the theoretical analysis, our algorithm is equivalent to a Gauss-Newton method solving an optimization problem, and it converges uniformly to the optimal solution. The online data can also be used repeatedly, which is highly efficient. Simulation results demonstrate its feasibility to solve the unknown nonlinear ZSG. When compared with other algorithms, it saves a significant amount of online measurement time. |
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